Ride-Hail Drivers, Taxi Drivers and Multiple Jobholders: Who Takes the Most Risks and Why?
Bibliographic record
Abstract
Little is known about how the use of ride-hail apps (e.g. Uber, Lyft) affects drivers’ propensity to engage in risky behaviours. Drawing on labour process theory, this study examines how algorithmic control of ride-hail drivers encourages risky driving (i.e. violating road safety rules, carrying weapons). Furthermore, the theory of work precarity is used to explain why multiple jobholders (MJHers), who work for ride-hail companies, drive taxis and hold other jobs, may be more likely to take risks while driving due to income insecurity and erratic work hours. The hypotheses are tested in a sample ( N = 191) of ride-hail drivers, taxi drivers and MJHers. The results suggest that MJHers are more likely to engage in risky driving in comparison to ride-hail and taxi drivers. Theoretical, practical and policy implications are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".